Systems and methods for generating a cancer alleviation nourishment plan

ABSTRACT

A system for generating a cancer alleviation nourishment plan includes a computing device configured to receive at least a cancer biomarker, retrieve a cancer profile, identify, using the cancer profile, a plurality of nutrition elements, including assigning the cancer profile to a cancer category, wherein the cancer category summarizes the current malignancy state of the user, calculating, according to the cancer category, a plurality of nutrient amounts, wherein calculating the plurality of nutrient amounts includes determining an effect on the cancer profile and calculating the plurality of nutrient amounts as a function of the effect, wherein the plurality of nutrient amounts is intended to result in cancer prevention, identifying, as a function of the plurality of nutrient amounts, the plurality of nutrition elements, wherein the plurality of nutrition elements contain the plurality of nutrient amounts, and generate, using the plurality of nutrition elements, the cancer prevention nourishment plan.

FIELD OF THE INVENTION

The present invention generally relates to the field of nutritionplanning for cancer prevention. In particular, the present invention isdirected to systems and methods for generating a cancer alleviationnourishment plan.

BACKGROUND

It has been estimated that 30-40 percent of all cancers may be preventedby lifestyle measures. Obesity, nutrient sparse foods such asconcentrated sugars and refined flour products that contribute toimpaired glucose metabolism, low fiber intake, consumption of red meat,and imbalance of omega fatty acids may all contribute to excess cancerrisk. Intake of particular ingredients, especially lignan fractions ofplants, and abundant portions of fruits and vegetables may have aneffect on cancer risk. Substantial experimental evidence indicates thepotential importance of dietary and nutritional factors in cancerprevention but identifying relationships between diet and cancer inobservational epidemiological and intervention trials has provedchallenging.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for generating a cancer alleviation nourishmentplan including a computing device configured to receive at least acancer biomarker relating to a user, retrieve a cancer profile relatedto the user, assign the cancer profile to a cancer category, wherein thecancer category includes a determination of a current malignancy stateof the user, identify, using the cancer profile, a plurality ofnutrition elements for the user, wherein identifying includescalculating, according to the cancer category, a plurality of nutrientamounts, wherein calculating the plurality of nutrient amounts includesdetermining a respective effect of each nutrient amount of the pluralityof nutrient amounts on the cancer profile, and calculating each of thenutrient amounts of the plurality of nutrient amounts as a function ofthe respective effect of each the plurality of nutrient amounts, whereinthe plurality of nutrient amounts comprises a plurality of amountsintended to result in cancer alleviation corresponding to the cancercategory, identifying, as a function of the plurality of nutrientamounts, the plurality of nutrition elements for cancer alleviation, andgenerate, using the plurality of nutrition elements, a canceralleviation nourishment plan.

In another aspect, a method for generating a cancer alleviationnourishment plan including receiving, by a computing device, at least acancer biomarker relating to a user, retrieving, by the computingdevice, a cancer profile related to the user, assigning, by thecomputing device, the cancer profile to a cancer category, wherein thecancer category includes a determination of a current malignancy stateof the user, identifying, by the computing device, using the cancerprofile, a plurality of nutrition elements for the user, whereinidentifying includes calculating, according to the cancer category, aplurality of nutrient amounts, wherein calculating the plurality ofnutrient amounts includes determining a respective effect of eachnutrient amount of the plurality of nutrient amounts on the cancerprofile, and calculating each of the nutrient amounts of the pluralityof nutrient amounts as a function of the respective effect of each theplurality of nutrient amounts, wherein the plurality of nutrient amountscomprises a plurality of amounts intended to result in canceralleviation corresponding to the cancer category, identifying, as afunction of the plurality of nutrient amounts, the plurality ofnutrition elements for cancer alleviation, and generating, by thecomputing device, using the plurality of nutrition elements, a canceralleviation nourishment plan.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating a system for generating a canceralleviation nourishment plan;

FIG. 2 is a block diagram illustrating a machine-learning module;

FIG. 3 is a block diagram of a cancer nourishment plan database;

FIGS. 4A and 4B are a diagrammatic representation of a cancer profile;

FIG. 5 is a diagrammatic representation of a cancer alleviationnourishment plan;

FIG. 6 is a diagrammatic representation of a user device;

FIG. 7 is a block diagram of a workflow of a method for generating acancer alleviation nourishment plan; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a cancer alleviation nourishmentplan. In an embodiment, system includes a computing device configured toreceive cancer biomarkers of a user. Cancer biomarkers may includeexperimental testing results, such as genetic sequencing data, bloodpanel, lipid panel, etc. Computing device is configured to retrieve acancer profile corresponding to the user. Computing device may generatecancer profile, by using a machine-learning algorithm to model cancerbiomarkers to malignancies parameters. Computing device may enumeratemalignancies parameters in the cancer profile, and classify the user toa cancer category, for instance using a machine-learning classifier.Computing device is configured to determine the effect of nutrients onthe user's cancer profile and calculate nutrient amounts according tothe effect that may prevent, or otherwise address, cancer biomarkersidentified of the user. Computing device may identify nutritionelements, such as an individual ingredients, and calculate a nourishmentplan, including combinations of the ingredients to achieve the calculatenutrition amounts. Computing device may accept user preferencesregarding nutrition elements and generate a cancer preventionnourishment plan, wherein items are curated according to the user'sunique cancer profile and nutrition element preferences. Participationand adherence to nourishment plan may be provided a nourishment scorefor tracking cancer prevention.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forgenerating a cancer alleviation nourishment plan is illustrated. Systemincludes a computing device 104. Computing device 104 may include anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. Computing device104 may include a single computing device operating independently, ormay include two or more computing device operating in concert, inparallel, sequentially or the like; two or more computing devices may beincluded together in a single computing device or in two or morecomputing devices. Computing device 104 may interface or communicatewith one or more additional devices as described below in further detailvia a network interface device. Network interface device may be utilizedfor connecting computing device 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Computing device 104 mayinclude but is not limited to, for example, a computing device orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. Computingdevice 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Computing device 104 may distribute one or more computing tasks asdescribed below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. Computing device 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1, computing device is configured toreceive at least a cancer biomarker related to a user. A “cancerbiomarker,” as used in this disclosure, is a biological and/or chemicalsubstance or process that is indicative of the presence of cancer in thebody. Cancer biomarker 108 may include biological molecules existingwithin a normal cell, a cancerous cell, secreted by a tumor, and/or aspecific response of the body to the presence of cancer. Receiving atleast the cancer biomarker 108 may include receiving a result of one ormore tests relating the user. Cancer biomarker 108 may include testresults of screening and/or early detection of cancer, diagnosticprocedures, prognostic indicators from other diagnoses, from predictorsidentified in a medical history, and information relating tobiomolecules associated with malignancy such as: ATM, BRCA 1, BRCA 2,BARD1, CDH1, CHEK2, EGFR, EPCAM, erB2, FANCC, KRAS, MLH1, MRE11, MSH2,MSH6, MUTYH, NBN, NF1, p52, PALB2, PMS2, PTEN, RAD50, STK11, TP53 (p53),XRCC, abnormal DNA methylation patterns, gene expression patterns, generegulation, the presence of particular miRNAs and other non-coding RNAs(ncRNAs), CA-125, CBC, blood protein testing, tumor marker testing,circulating tumor cell tests, flow cytometry, thyroglobulin, and thelike. A person skilled in the art having the benefit of the entirety ofthis disclosure will be aware of various additional tests and/orbiomarkers that may be used and or received to receive cancer biomarker.

Continuing in reference to FIG. 1, such a test may include resultsenumerating the identification of mutations in DNA sequences. Testresults may indicate the presents of single nucleotide polymorphisms(SNPs) in genetic sequences. Test results may indicate epigeneticfactors indicative of cancer. Cancer biomarker 108 may includehematological analysis including results from T-cell activation assays,abnormal nucleation of white blood cells, white blood cell counts,concentrations, recruitment and localization, and the like. Cancerbiomarker 108 may be received as a function of a user indicating a priordiagnosis, XRT treatment, chemotherapy regimen, etc., such as “currentmedications,” wherein one is a cancer treatment. Cancer biomarker 108may include any cancer-related symptoms, side effects, andco-morbidities associated with and relating to cancer diagnosis,treatment and/or remission, such as metallic taste in mouth fromchemotherapy, decreased bone density after chemotherapy, skinburning/rash/scarring from radiation treatment, hair loss, nail beddamage, onset of sclerosis, foggy memory, etc. Cancer biomarker 108 maybe received and/or identified from a biological extraction of a user,which may include analysis of a physical sample of a user such as blood,DNA, saliva, stool, and the like, without limitation and as described inU.S. Nonprovisional application Ser. No. 16/886,647, filed May 28, 2020,and entitled, “METHODS AND SYSTEMS FOR DETERMINING A PLURALITY OFBIOLOGICAL OUTCOMES USING A PLURALITY OF DIMENSIONS OF BIOLOGICALEXTRACTION USER DATA AND ARTIFICIAL INTELLIGENCE,” the entirety of whichis incorporated herein by reference.

Continuing in reference to FIG. 1, cancer biomarker 108 may be organizedinto training data sets. “Training data,” as used herein, is datacontaining correlations that a machine learning process, algorithm,and/or method may use to model relationships between two or morecategories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine learningprocesses as described in further detail below.

Continuing in reference to FIG. 1, cancer biomarker 108 may be used togenerate training data for a machine-learning process. A “machinelearning process,” as used in this disclosure, is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm (such as a collection of one ormore functions, equations, and the like) that will be performed by amachine-learning module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning softwareprograming where the commands to be executed are determined in advanceby a subject and written in a programming language, as described infurther detail below.

Continuing in reference to FIG. 1, cancer biomarker 108 may be organizedinto training data sets and stored and/or retrieved by computing device104, without limitation, as a relational database, a key-value retrievaldatabase such as a NOSQL database, or any other format or structure foruse as a database that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Cancerbiomarker 108 training data may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table and the like. Cancerbiomarker 108 training data may include a plurality of data entriesand/or records, as described above. Data entries may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data entries of cancer biomarkers may bestored, retrieved, organized, and/or reflect data and/or records as usedherein, as well as categories and/or populations of data consistent withthis disclosure.

Continuing in reference to FIG. 1, computing device is configured toretrieve a cancer profile related to the user. A “cancer profile,” asused in this disclosure, is a profile that summarizes a user's currentstate with regard to cancer. Cancer profile 112 may include at least amalignancy parameter, A “malignancy parameter,” as used in thisdisclosure, is a quantitative metric that encapsulates a current stateof cancer in the user according to the presence of at least a cancerbiomarker 112. A current state of cancer may include a currentpropensity for developing a malignancy. A current state of cancer mayinclude “no malignancy”. In individuals harboring no malignancy, acurrent state of cancer may include a tissue, organ, cancer type, etc.,with which the user most closely resembles, or has a likelihood ofdeveloping in the future. Malignancy parameter may bemalignancy-specific, for instance and without limitation, a numericalvalue for each of 100+ cancer types, where the numerical value is alikelihood that a cancer biomarker 108 relates to a solid tumor, ametastasis, a particular cancer, etc. Cancer profile 112 may include anymedical, physiological, biological, chemical, and/or physicaldetermination about the current state of a user's propensity for cancer,including their “current likelihood for cancer”, and projected, futurelikelihood for cancer. Cancer profile 112 may include qualitative and/orquantitative (malignancy parameter) summarization of the presence ofmalignant tissue, metastasis, solid tumors, circulating tumor cells,biomarkers indicative of cancer, current risk of cancer, future risk ofcancer, lifetime risk of cancer, biomarkers classified to cancer types,and the like. Cancer profile 112 may include qualitative determinations,such as binary “yes”/“no” determinations for cancer types,“normal”/“abnormal” determinations about the presence of and/orconcentration of cancer biomarkers 108, for instance as compared to anormalized threshold value of a biomarker among healthy adults. Cancerprofile 112 may include a plurality of malignancy parameters, whereinmalignancy parameters are quantitative determinations such as a “cancerscore”, which may include any metric, parameter, or numerical value thatcommunicates a cancer state. Cancer profile 112 may include malignancyparameters that are mathematical representations of the current state ofcancer, such as a function describing the cancer risk as a function oftime. Malignancy parameters may be cancer-specific, tissue-specific,biological pathway-specific, etc. Cancer profile 112 may includeinstantaneous cancer risk, such as weekly, monthly, annual, etc., cancerrisk, classified by cancer type, according to medical history,biological extraction result, and the like.

Continuing in reference to FIG. 1, retrieving cancer profile 112 mayinclude receiving cancer profile training data. “Cancer profile trainingdata” may include cancer biomarkers 112 organized into training datasets, as described above, including results from biological extractionsamples, health state questionnaires regarding symptomology, medicalhistories, physician assessments, lab work, and the like. Cancer profiletraining data may originate from the subject, for instance via aquestionnaire and a user interface with computing device 104, for userto provide medical history data. Receiving cancer profile training datamay include receiving whole genome sequencing, gene expression patterns,and the like, for instance as provided by a genomic sequencing entity,hospital, database, the Internet, etc. Cancer profile training data mayinclude raw data values recorded and transmitted to computing device 104via a wearable device such as a pedometer, gyrometer, accelerometer,motion tracking device, bioimpedance device, ECG/EKG/EEG monitor,physiological sensors, blood pressure monitor, blood sugar and volatileorganic compound (VOC) monitor, and the like. Cancer profile trainingdata may originate from an individual other than user, including forinstance a physician, lab technician, nurse, caretaker, psychologist,therapist, and the like. Cancer profile training data may be input intocomputing device 104 for instance via a health state questionnaire foronboarding of user symptomology, via a graphical user interface. It isimportant to note that training data for machine-learning processes,algorithms, and/or models used in system 100 herein may originate fromany source described for cancer profile training data.

Continuing in reference to FIG. 1, a “graphical user interface,” as usedin this disclosure, is any form of a user interface that allows a userto interface with an electronic device through graphical icons, audioindicators, text-based interface, typed command labels, text navigation,and the like, wherein the interface is configured to provide informationto the user and accept input from the user. Graphical user interface mayaccept input, wherein input may include an interaction (such as aquestionnaire) with a user device. A user device, as described infurther detail below, may include computing device 104, a “smartphone,”cellular mobile phone, desktop computer, laptop, tablet computer,internet-of-things (JOT) device, wearable device, among other devices.User device may include any device that is capable for communicatingwith computing device 104, database, or able to receive data, retrievedata, store data, and/or transmit data, for instance via a data networktechnology such as 3G, 4G/LTE, 5G, Wi-Fi (IEEE 802.11 family standards),and the like. User device may include devices that communicate usingother mobile communication technologies, or any combination thereof, forshort-range wireless communication (for instance, using Bluetooth and/orBluetooth LE standards, AirDrop, Wi-Fi, NFC, etc.), and the like.

Continuing in reference to FIG. 1, retrieving cancer profile 112 mayinclude training a cancer profile machine-learning model with cancerprofile training data that includes a plurality of data entries whereineach entry correlates cancer biomarkers 112 to a plurality of malignancyparameters. Cancer profile machine-learning model 116 may include anymachine-learning algorithm (such as K-nearest neighbors algorithm, alazy naïve Bayes algorithm, etc.), machine-learning process (such assupervised machine-learning, unsupervised machine-learning), or method(such as neural nets, deep learning, etc.). Cancer profilemachine-learning model 116 may be trained to derive the algorithm,function, series of equations, or any mathematical operation,relationship, or heuristic, that can automatedly accept an input (cancerbiomarker(s) 108) and correlate, classify, or otherwise calculate anoutput (malignancy parameter(s)). Cancer profile machine-learning model116 may include individual functions, derived for unique relationshipsobserved from the training data for each cancer biomarker 108. Innon-limiting illustrative examples, the expression levels of a varietyof oncogenic genes in human tissues, as identified above, may beretrieved from a database, such as a repository of peer-reviewedresearch (e.g. National Center for Biotechnology Information is part ofthe United States National Library of Medicine), and the cancer profilemachine-learning model 116 derived algorithm may observe an average andstatistical evaluation (mean±S.D.) may be calculated from the data,across which the user's expression level is compared. In such anexample, cancer profile machine-learning model 116 may derive analgorithm according to the data which may also include a scoringfunction that includes a relationship for how to arrive at a malignancyparameter numerical value score according to the user's level of geneexpression (e.g. number of mRNA transcripts per tissue) as it relates tothe average and statistical evaluation in normal tissue expression.

Continuing in reference to FIG. 1, cancer biomarker 108 may becorrelated to a plurality of malignancy parameters without the use ofmachine-learning process(es). For instance and without limitation,computing device 104 may use a web browser and the Internet to identifya plurality of threshold values of gene expression that relate to cancerbiomarkers 108 in “healthy adults”, wherein gene expression values thatdeviate from such a threshold may indicate malignancy, and the magnitudeof deviation relates to the magnitude of numerical value for malignancyparameter.

Continuing in reference to FIG. 1, retrieving the cancer profile 112 mayinclude generating the cancer profile 112 using the cancer profilemachine-learning model 116 and at least the cancer biomarker 108.Persons skilled in the art may appreciate that cancer profile 112 maybecome increasingly more complete, and more robust, with increasingnumbers of malignancy parameters, describing larger sets of cancerbiomarkers 108 in the user. Malignancy parameter may be generated foreach gene (or set of genes) described above; each white blood cell type(or set of white blood cell type); among other factors. Cancer profilemachine-learning model 116 may derive a unique algorithm for developingindividual malignancy parameters from the plurality of cancer biomarkers108. Cancer profile machine-learning model 116 may derive functions,systems of equations, matrices, etc., that describe and/or incorporaterelationships between sets of cancer biomarkers 108 (training data), forinstance combining the expression level of two or more genes, multipliedby scalar coefficients according to the presence of SNPs (singlenucleotide polymorphisms) or mutations present in the genes, dividing bythe ratio of phosphorylated-unphosphorylated states, ubiquitinatedstates, etc. In the full spectrum of cell signaling, maintainingcellular homeostasis, cell division, protein degradation, among otherbiological phenomenon that may contribute to the development of cancers,cancer profile machine-learning model 116 may derive increasinglycomplicated algorithms for combining cancer biomarkers 108 intomalignancy parameters summarized in cancer profile 112.

Continuing in reference to FIG. 1, computing device 104 is configured toidentify, using the cancer profile 112, a plurality of nutritionelements for the user. A “nutrition element,” as used in thisdisclosure, is any item that includes a nutrient intended to be usedand/or consumed by user for cancer alleviation. A “nutrient,” as used inthis disclosure,” is any biologically active compound whose consumptionis intended for the treatment and/or prevention of cancer. Nutritionelement 120 may include alimentary elements, such as meals (e.g. chickenparmesan with Greek salad and iced tea), food items (e.g. French fries),grocery items (e.g. broccoli), health supplements (e.g. whey protein),beverages (e.g. orange juice), and the like. Nutrition element 120 maybe “personalized” in that nutrition elements are curated in a guidedmanner according to cancer profile 112, gene expression patterns, cancerbiomarkers 108, SNPs, the Warburg Effect, a cancer category (liver,lung, pancreatic, brain, breast, blood, carcinoma, melanoma, Stage I,Stage II, etc.), treatment type (T-Car therapy, hormone treatment,surgery, taxanes, cisplatin, etc.), and the like. Nutrition element 120may include supplementary use of oral digestive enzymes and probioticswhich may also have merit as anticancer measures. Nutrition elements 120in a cancer prevention diet may include selenium, folic acid, vitaminB-12, vitamin D, chlorophyll, and antioxidants such as the carotenoids(α-carotene, β-carotene, lycopene, lutein, cryptoxanthin). Nutrientelements 120 may contain biological active compounds that are nottypically considered vitamins and/or minerals, nor are they intended toprovide appreciable amounts of calories, such as phytonutrients andantioxidants; for instance allium and bioactive ingredients present incruciferous vegetables such as broccoli sprouts, which are known sourcesof antioxidants such as sulforaphane, which may have therapeutic effectson cancerous cells. Nutrition elements 120 may include a specificdietary category, such as a “ketogenic diet”, “low glycemic index diet”,“Paleo diet”, and so on.

Continuing in reference to FIG. 1, identifying the plurality ofnutrition elements 120 includes assigning the cancer profile 112 to acancer category, wherein the cancer category is a determination about acurrent malignancy state of the user. A “cancer category,” as used inthis disclosure, is a designation of a cancer type. Cancer category 124may include tissue or organ type, such as “liver cancer”, “lung cancer”,“skin cancer”, etc. Cancer category 124 may include a designationregarding a cancer type that may not involve a particular tissue such as“sarcoma”, “carcinoma”, “lymphoma”, etc. Cancer category 124 may includepathological, histological, and/or clinical classification identifierssuch as “Stage I-IV” classification system, presence of metastasis,spread to lymph nodes, etc. Cancer category 124 may include identifiersassociated with metastasis, remission rates, and survivability. Cancercategory 124 may include a predictive cancer classification, where auser does not currently harbor a particular malignancy but may includedata that indicates a cancer category 124 with which they may be mostclosely categorized to. For instance, a family history of breast cancerdue to a combination of hereditary genetic elements (as summarized incancer profile 112) may classify an individual in “breast cancer” cancercategory 124, despite not currently having breast cancer. Cancer profile112 may have associated with it an identifier, such as a label, thatcorresponds to a cancer category 124.

Continuing in reference to FIG. 1, assigning the cancer profile to acancer category may include classifying the cancer profile 112 to acancer category 124 using a cancer classification machine-learningprocess. Classification using cancer classification machine-learningprocess 128 may include identifying which set of categories (cancercategory 124) an observation belongs (cancer profile 112).Classification may include clustering based on pattern recognition,wherein the presence of cancer biomarkers 108, such as geneticindicators, symptoms, and the like, identified in cancer profile 112relate to a particular cancer category 124. Such classification methodsmay include binary classification, where the cancer profile 112 issimply matched to each existing cancer category 124 and sorted into acategory based on a “yes”/“no” match. Classification done in such amanner may include weighting, scoring, or otherwise assigning anumerical value to elements in cancer profile 112 as it relates to eachcancer type and assign a user to a cancer category 124 for the cancertype that results in the highest score. Such a score may represent a“likelihood”, probability, or other numerical data that relates to theclassification into cancer category 124.

Continuing in reference to FIG. 1, cancer classificationmachine-learning process 128 may include any machine-learning process,method, and/or algorithm, as described in further detail below. Cancerclassification machine-learning process 128 may generate a “classifier”using training data. A classifier may include a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below. Such a classifier may sort inputs(such as the data in the cancer profile 112) into categories or bins ofdata (such as classifying the data into a cancer category), outputtingthe bins of data and/or labels associated therewith. Training data usedfor such a classifier may include a set of cancer profile 112 trainingdata as it relates to classes of cancer types, organ/tissue, types, etc.For instance, training data may include ranges of biomarkers as theyrelate to various cancer types, severity of cancer (Stage I-IV), and thelike. Using datasets of this data as training data to train a classifierto derive relationships present in the data that may result in amachine-learning model that automatedly classifies a user to a cancercategory as a function of the data present in their cancer profile 112.A classifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Machine-learning module, as described in further detail below. maygenerate a classifier using a classification algorithm, defined as aprocess whereby a computing device and/or any module and/or componentoperating thereon derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. As anon-limiting example, a cancer profile 112 training data classifier mayclassify elements of training data to elements that characterizes asub-population, such as a subset of cancer biomarker 108 (such as geneexpression patterns as it relates to a variety of cancer types) and/orother analyzed items and/or phenomena for which a subset of trainingdata may be selected.

Continuing in reference to FIG. 1, classifying the cancer profile 112(input) to a cancer category 124 (output) may include assigning thecancer category 124 as a function of the cancer classificationmachine-learning process 128 and the cancer profile 112. Training datamay include sets of malignancy parameters and/or cancer biomarkers 108,as described above. Such training data may be used to “learn” how tocategorize a user's cancer profile 112 to cancer categories depending ontrends in mutations, gene expression, SNPs, user symptomology, and thelike. Training data for such a classifier may originate from user input,for instance via a health state questionnaire via a graphical userinterface, may originate from a biological extraction test result suchas genetic sequencing, blood panel, lipid panel. Training data mayoriginate from a user's medical history, a wearable device, a familyhistory of disease. Training data may similarly originate from anysource, as described above, for cancer biomarker 108 and determiningcancer profile 112.

Continuing in reference to FIG. 1, identifying the plurality ofnutrition elements 120 includes calculating, according to the cancercategory 124, a plurality of nutrient amounts, wherein calculating theplurality of nutrient amounts includes determining a respective effectof each nutrient amount of the plurality of nutrient amounts on thecancer profile 112. An “effect of a nutrient,” as used in thisdisclosure, is a change, consequence, and/or result in at least a cancerbiomarker 108, cancer profile 112, cancer category 124, and/orlikelihood of cancer in a user due to consumption of an amount of anutrient. An effect of a nutrient may be “no effect”. Calculating aneffect of a nutrient may include determining how a cancer biomarker 108may change, such as an increase/decrease according to a particularamount of nutrient. For instance and without limitation, such acalculation may include determining the effect of chronic, sustainednutrient amounts in a diet for weeks, months, etc.

Continuing in reference to FIG. 1, determining the effect of theplurality of nutrient amounts on the cancer profile 112 may includeretrieving a plurality of predicted effects of each nutrient amount ofthe plurality of nutrient amounts on the cancer profile 112 as afunction of at least the cancer biomarker 108. A “predicted effect” of anutrient or combination of nutrients as used in this disclosure, is ahypothesis about the outcome for a user after consuming a nutrientamount and/or amount of a combination of nutrients. Retrieving aplurality of predicted effects may include retrieving from a database, aresearch repository, or the like. Retrieving a plurality of effects mayinclude, for instance, searching using the cancer profile 112, a webbrowser and the Internet, for a plurality of effects. In someembodiments retrieving a plurality of predicted effects may includecalculating at least an effect, for instance by deriving a function froma machine-learning algorithm. A predicted effect of a plurality ofnutrient amounts may include the effect on cancer category 124, cancerbiomarker 108, malignancy parameter, likelihood of cancer, cancer risk,etc. from a particular nutrient amount, or combination of nutrientamounts.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, determining an effect of a nutrient may include determining ifa change in cancer category 124 may arise from adding and/or removing anutrient from a user's diet, for instance changing a cancer category 124from “skin cancer” to “gastric cancer” with increasing dietary vitamin Eand vitamin K by introducing nutrition elements 120 a user is notaccustomed (e.g. vegetable oils, soybeans, tree nuts, seeds, green leafyvegetables, etc.). Calculating an effect of a nutrient may include amathematical operation, such as subtraction, addition, etc. Calculatingan effect of a nutrient may include retrieving an empirical equationthat describes relationships between a nutrient and cancer biomarker108, test results, malignancy parameter, and the like. Calculating aneffect of a nutrient may include deriving an algorithm, function, or thelike, for instance using a machine-learning process and/or model.Calculating such an effect using machine-learning may include trainingdata that includes a plurality of nutrients as it relates to effects oncancer categories 124, cancer biomarkers 108, etc.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, oral vitamin C doses used past studies may produce peak plasmaconcentration of less than 200 micromolar (μM). In contrast, the samedose given intravenously, as used in the Pauling studies, would producepeak plasma concentrations of nearly 6 mM, more than 25 times higher.When given orally, vitamin C concentration in human plasma is tightlycontrolled by multiple mechanisms acting together: intestinalabsorption, tissue accumulation, renal reabsorption, and excretion, andpotentially even the rate of utilization. However, when ascorbate isadministered intravenously or intraperitoneally the tight controls arebypassed, and pharmacologic millimolar plasma concentrations of vitaminC can easily be achieved. For example, phase I clinical studies revealedthat ascorbate concentrations could safely reach 25-30 mM withintravenous infusion of 100 g of vitamin C, and thus super-high dailyvitamin C dosages may also be supported. Plasma concentrations of up to10 mM may be sustained for at least 4 hours which, based on preclinicalstudies, is sufficient to have an effect on cancer cells. Given the factthat cancer patients were only treated with vitamin C orally in thestudies, the studies provide some evidence that high dose vitamin Cefficacy may have efficacy in some patients. And, over the past decade,there have been an increased number of phase I/II clinical trials andcase reports testing the safety and efficacy of high dose vitamin C as atreatment for various cancer patients, specifically as a conjunctivetherapy in addressing chemotherapy-induced toxicity and co-morbidity.Thus, there is mounting evidence that specific cancer state to nutrientrelationships may be found and observed in clinical data. And such datamay reveal specific oral dosage to plasma concentration effects of eachnutrient amount, wherein the nutrient amount may be found to beincreased far above what would be normally considered to maintain aspecific effect in a particular cancer patient.

Continuing in reference to FIG. 1, computing device 104 is configuredfor calculating each of the nutrient amounts of the plurality ofnutrient amounts as a function of the respective effect of each theplurality of nutrient amounts, wherein the plurality of nutrient amountscomprises a plurality of amounts intended to result in canceralleviation corresponding to the cancer category. Calculating nutrientamounts, may include determining an effect of a nutrient on theplurality of malignancy parameters in the cancer profile 112, whereinthe effect of the nutrient is correlated to the malignancy parameter. A“nutrient amount,” as used in this disclosure, is a numerical value(s)relating to the amount of a nutrient. Nutrient amount 132 may includemass amounts of a vitamin, mineral, macronutrient (carbohydrate,protein, fat), a numerical value of calories, mass amounts ofphytonutrients, antioxidants, probiotics, nutraceuticals, bioactiveingredients, and the like. For example and without limitation, utilizinghigh doses of vitamin C may have an effect on malignancy parameters,which in-turn effect cancer profile 112. Cancer patients often presentwith severely low levels of vitamin C in the blood and featurescurvy-like symptoms, leading researchers to postulate that vitamin Cmay protect against cancer specifically by increasing collagensynthesis. However, this may have an effect on malignancy parameters,such as parameters describing metastasis and metastatic potential inpatients. Researchers hypothesized that ascorbate could suppress cancerdevelopment by inhibiting hyaluronidase, which otherwise weakens theextracellular matrix and enables cancer to metastasize. Therefore, insuch patients, vitamin C supplementation above what may be normallyconsidered “recommended” may have an effect for increasing malignancyparameters associated with metastasis.

Continuing in reference to FIG. 1, computing device 104 may calculatenutrient amounts 132, for instance, by using a default amount, such asfrom a standard 2,000 calorie diet, and increasing and/or decreasing theamount according to a numerical scale associated with malignancyparameters in the cancer profile 112. Such a calculation may include amathematical operation such as subtraction, addition, multiplication,etc.; alternatively or additionally, such a calculation may involvederiving a loss function, vector analysis, linear algebra, system ofquestions, etc., depending on the granularity of the process. Derivingsuch a process for the calculating may include machine-learning.Nutrient amounts 132 may include threshold values, or ranges or values,for instance and without limitation, between 80-120 mg vitamin C per 24hours, wherein the range changes as a function of cancer profile 112.Nutrient amounts 132 may be calculated as heat maps (or similarmathematical arrangements), for instance using banding, where each datumof cancer profile 112 elicits a particular range of a particularnutrient amount 132 or set of amounts. In non-limiting illustrativeexamples, such a calculation may include querying for and retrieving astandard amount of water soluble vitamins for a healthy adult, forinstance as described below in Table 1:

TABLE 1 Nutrient Amount Vitamin C   60 mg/day Thiamin (B1)  0.5 mg/1,000kcal; 1.0 mg/day Riboflavin (B2)  0.6 mg/1,000 kcal; 1.2 mg/day Niacin(B3)  6.6 NE/1,000 kcal; 13 ND/day Vitamin B6 0.02 mg/1 g protein; 2.2mg/day Vitamin B12   3 μg/day Folic Acid  400 μg/day

Continuing in reference to FIG. 1, in reference to Table 1 above,wherein NE is niacin equivalent (1 mg niacin, or 60 mg tryptophan), mg(milligram), kcal (1000 kcal=1 Calorie), and μg (microgram). Computingdevice 104 may store and/or retrieve the above standard nutrientamounts, for instance in a database. The amounts may be re-calculatedand converted according to a user's cancer profile 112. For instance,these amounts may relate to an average BMI, healthy adult male, for anyrange of calories, but may be adjusted according to unique user-specificcancer biomarkers 108. In non-limiting illustrative examples, an obesewoman who is on a 1,400 Calorie/day diet, curated according toidentified risk factors (cancer biomarkers 108) may need the aboveamounts recalculated according to such a diet, where some amounts mayincrease, some may decrease, and some may remain constant. For instance,if such a person were to suffer from leukemia, a particular increaseamong vitamin C may be calculated according to a weighting factorassociated with leukemia; with colon cancer, vitamin C may increase by adifferent amount, but vitamin A from retinol sources (animal products)may need to decrease, and so on among many other cancer types.

Continuing in reference to FIG. 1, calculating nutrient amounts 132 mayinclude deriving a weighting factor to adjust, or otherwisere-calculate, an amount. Weighting factor may be determined by computingdevice 104, for instance, by querying for vitamin amounts according todata inputs identified in the cancer profile 112. For instance innon-limiting illustrative examples, if cancer profile 112 indicates thepresence of mutant forms of the BRCA1 and/or BRCA2 gene, vitaminsinvolved in DNA-damage response pathways such as niacin and vitamin B6may be increased in the diet by varying amounts. BRCA1/2 genes encodefor proteins intimately involved in the DNA damage response (DDR).Mutant forms of DDR proteins may result in accumulated DNA damage thatultimately results in cancer formation. Prevention of cancer formationover the lifetime of the user in this manner may be achieved withsupplementation of niacin, vitamin B6, among other nutrients such asbiomolecules that can mitigate oxidative damage to DNA and alleviate thestress on the cell due to mutation in these genes. Although, niacin fromorganic sources (food items) may be superior from nonorganic sources(commercially-available supplements) from a bioavailability standpoint.Additionally, per-user pharmacokinetics, rates of metabolism and/oradsorption of niacin may differ user-to-user, which may negate theeffectiveness of proscribing particular diet types and nutritionelements 120 to users. In such an instance, computing device 104 mayaccount for such details using machine-learning to derive more specificnutrient amount 132 calculations and to more accurately calculate theamounts by which to increase/decrease niacin and vitamin B6 for thepresence of the BRCA1/2 mutation.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, such a machine-learning process may employ a machine-learningalgorithm to derive per-user pharmacokinetics of vitamin B6. Themachine-learning algorithm may accept an input of values including thetotal amount of protein consumed (in grams) and total amount of vitaminB6 consumed (in mg) per day in a diet, and what the serum levels of thevitamin B6 vitamer, pyridoxal-5-phosphate, over the course of a month,and derive the rates of metabolism, or how ‘well’ the user is obtainingthe vitamin from nutrition elements 120 and adsorbing vitamin B6. Inother words, the algorithm may derive a function (e.g. using linearregression, vector quantization, least squares, etc.) that describes thepharmacokinetics for that particular user regarding what amount ofvitamin B6 consumed, per amount of dietary protein, results in whatcorresponding amount of bioactive vitamin compound, as measured by theblood vitamer from a biological extraction. Such a function, obtainedfrom machine-learning, may then be used by computing device 104 with aninput of the cancer profile 112, which enumerates the expression level(e.g. amount of BRCA1/2 RNA transcripts in tissue, protein expressionlevel in the cell, etc.) and/or presence of BRCA1/2 mutation, tocalculate an output which is a more accurate, customized, per-usernutrient amount 132 of vitamin B6. Persons skilled in the art mayappreciate that this process may be repeated and completed for the fullspectrum of nutrients, both required as part of a diet and not requiredas part of a diet.

Continuing in reference to FIG. 1, additionally, in non-limitingillustrative examples, computing device 104 may relate theconcentrations of the metabolic products related to vitamins (e.g.vitamers), minerals, phytonutrients, antioxidative compounds, prodrugs,etc., to their effective concentrations in tissues related to variouscancer categories 124 in cancer profile 112. For instance, computingdevice 104 may additionally search and retrieve data that relates theblood levels of the vitamin B6 vitamer, pyridoxal-5-phosphate, to theeffective concentrations of vitamin B6 in breast tissue, which isparticularly sensitive to aberrations in the DDR from BRCA1/2 mutations.Computing device 104 may store the values in a “look-up table”, or grapha relationship as a mathematical function, among other ways ofrepresenting a data structure that relates the data identified in thesearch. Alternatively or additionally, computing device 104 may derive afunction, for instance using machine-learning, which relates theconcentration of the compound in a particular biological extraction,such as blood, to varying amounts in tissues such as breast tissue,liver, kidneys, etc. This may prove helpful in calculating nutrientamounts 132 as a function of user consumption to specific targetnutrient amount 132 quantities within a particular organ/tissueaccording to the input data in the cancer profile 112.

Continuing in reference to FIG. 1, identifying the plurality ofnutrition elements 120 according to the cancer category 124 may includetraining a machine-learning model. For instance and without limitation,training data for a machine-learning model may include nutritionelements 120 associated with, or correlated to, cancer categories 124,such as “colon cancer” nutrition elements 120, “glioblastoma” nutritionelements 120, etc. Training data may be used to train machine-learningmodel to derive an algorithm, which may automatedly return an output(plurality of nutrition elements 120) according to 1) cancer category124 and 2) nutrient amounts 132 for every input (cancer profile 112). Insuch an instance, machine-learning model may include anymachine-learning process, algorithm, and/or method, performed by amachine-learning module and computing device 104, as described infurther detail below. Such a machine-learning model may include aclassifier, which automatedly classifies nutrition elements 120 intocategories according to cancer category 124 so that computing device 104may “learn” which nutrition elements 120 to return as outputs accordingto relationships identified.

Continuing in reference to FIG. 1, calculating personalized nutrientamounts 132 may include generating training data using the plurality ofnutrition elements 120 identified according to the cancer category 124.Curated nutritional elements 120 may include generating training dataaccording to the plurality of nutrition elements 120 identifiedaccording to the cancer category 124. Generating training data mayinclude retrieving the elements and constructing a file, data structure,with or without labels, identifying factors, or the like. Training datamay originate from any source, as described above.

Continuing in reference to FIG. 1, calculating personalized nutrientamounts 132 may include training a nutrition machine-learning model 136according to the training data, wherein training data includes aplurality of data entries that correlates the nutrition elements 120 foreach cancer category 124 to nutrient amounts 132. Nutritionmachine-learning model 136 may include any machine-learning modeldescribed herein, as performed by a machine-learning module, describedin further detail below. Nutrition machine-learning model 136 may trainwith training data that includes nutrition elements 120 identified foreach cancer category 124 to derive relationships in nutrient amounts 132that relate to particular cancer category 124. For instance and withoutlimitation, foods identified to be associated with particular cancersmay reveal patterns in nutrients that have yet to be identified byphysicians, cancer biologists, dieticians, and the like. Trainednutrition machine-learning model 136 may generate a function (or seriesof functions) which describe alterations to nutrient amounts 132calculated directly from cancer profile 112, prior to classification tocancer category 124. In non-limiting illustrative examples, it may beshown that fiber content, which is oftentimes classically reported in ageneric sense as “carbohydrates”, is important for particular gastriccancers and colon cancers. Patterns may identify that plant-based diets,supplemented with particular bacterial species of probiotics may resultin personalized nutrient amounts 132 for cancer profiles 112 classifiedto those cancer categories 124.

Continuing in reference to FIG. 1, calculating nutrient amounts 132 mayinclude calculating nutrient amounts 132 as a function of the nutritionmachine learning model 136 and the cancer category 124. Trainednutrition machine learning model 136 may accept an input of cancerprofile 112 (and associated cancer category 124) to output nutrientamounts 132. Nutrient amounts 132 may be calculated using a variety offunctions, systems of equations, and the like, derived from mathematicalrelationships and/or heuristics identified in training data, forinstance from nutrition elements 120 identified from cancer categories124. Persons skilled in the art may appreciate that each cancer category124, of 100+ different types of cancers, may have a unique algorithm foridentifying nutrient amounts 132, of the 100's of distinct nutrientsidentified. For instance and without limitation, each cancer type,tissue/organ type, cancer stage, age of person, cancer biomarker 108,cancer profile 112, etc., may elicit a different mathematical equationfor calculating vitamin C. Wherein, vitamin C is one of manywater-soluble vitamins, and that each vitamin of that class may have adifferent equation associated with calculating nutrient amounts 132.Each equation may be derived by nutrition machine learning model 136according to the training data. Additionally, each user's specificpharmacokinetics, current dietary patterns, and the like, may add aunique step in the calculation, wherein the calculated nutrient amount132 is further personalized.

Continuing in reference to FIG. 1, identifying the plurality ofnutrition elements 120 includes identifying the nutrition elements 120according to the cancer category 124. Identifying nutrition element 120according to cancer category 124 may include querying, for instanceusing a web browser and the Internet, for foods, supplements, bioactiveingredients, and the like, which are correlated with a particular cancercategory 124. For instance and without limitation, computing device 104may organize a search for foods intended for “colon cancer”, wherein anentire diet may be crafted around target nutrient amounts 132 and thecategorization of the cancer profile 112 to “colon cancer”. In such anexample, the nutrition elements 120 are outputs generated from an inputsearch criteria of “colon cancer”. The output elements become“personalized” as they are arranged into daily, weekly, monthly, etc.,individual meals and/or meal schedule according to a user's particularcalculated nutrient amounts 132. The cancer category 124 may serve as afiltering step, wherein a search is guided by the cancer profile 112 asit was classified to a cancer type.

Continuing in reference to FIG. 1, identifying the plurality ofnutrition elements 120 includes identifying, as a function of theplurality of nutrient amounts 132, the plurality of nutrition elements120, wherein the plurality of nutrition elements 120 are intended toprevent cancer as a function of the cancer category 124. Cancer profile112 may be associated with user that does not currently have a cancerbelonging to cancer category 124. In such an instance, nutritionelements 120 may be “personalized” to an individual in that they areintended to prevent, as described above, cancer in that individual.Nutrition element 120 may prevent cancer in that they provide a nutrientintended to meet individualized, calculated nutrient amounts 132.Nutrient element 120 may prevent cancer in addressing the accumulationof heavy metals, such as lead, mercury, and cadmium, in a user.Nutrition element 120 may prevent cancer in addressing risk factorsassociated with the development of cancer such as exposure to asbestos,glass dust, fibers, and other particulate matter. Nutrition element 120may include foods and supplements intended to address genetic and cancerbiomarker 108 issues that are unique to each individual. “Curating”nutritional elements 120, as used in this disclosure, is a process ofcombining ingredients and/or nutrients according to calculated nutrientamounts 132. Curated nutritional elements 120 may include combiningingredients such as spices, plant-based materials, animal products,probiotic cultures, and the like, to result in a custom nutritionalelement 120, such as a particular “health shake”, unique dish, or thelike.

Continuing in reference to FIG. 1, computing device 104 is configured toidentify, as a function of the calculated nutrient amounts 132, theplurality of nutrition elements 120, wherein the plurality of nutritionelements 120 are intended to address a datum in the cancer profile 112.Nutrition elements 120, “intended to address a datum in the cancerprofile 112,” may refer to the process(es) of cancer treatment and/orprevention. “Cancer treatment,” as used in this disclosure, is theamelioration of cancer symptomology; such as nutrition elements 120intended for a person currently diagnosed with cancer and completing XRTtreatment (radiation therapy) and/or chemotherapy regimen. “Cancerprevention,” as used in this disclosure, is the reduction in risk forcancer; cancer prevention may include specifically curated nutritionelements 120 according to nutrient amount 124 that contain predeterminedrelationship regarding the lifetime risk of cancer, wherein the risk isdecreased if nutrient targets are achieved.

Continuing in reference to FIG. 1, computing device 104 may identify theplurality of nutrition elements 120 by using nutrient amount 124 as aninput and generating combinations, lists, or other aggregates ofnutrition elements 120 necessary to achieve nutrient amount 124. Forinstance, computing device 104 may use a template nutrient amount 124 of‘200 mg vitamin C’ and build a catalogue of nutritional elements 120until the 200 mg vitamin C value is obtained. Computing device 104 mayperform this task by querying for food items, for instance from a menu,grocery list, or the like, retrieving the vitamin C content, andsubtracting the value from the nutrient amount 124. In non-limitingillustrative examples, computing device 104 may identify orange juice(90 mg vitamin C/serving; 200 mg−90 mg=110 mg) for breakfast, Brusselsprouts (50 mg vitamin C/serving; 110 mg−50 mg=60 mg) for lunch, andbaked potato (20 mg vitamin C/serving) and spicy lentil curry (40 mgvitamin C/serving; 60 mg−(20 mg+40 mg)=0 mg) for dinner. In such anexample, computing device 104 may search according to a set ofinstructions (e.g. food preferences, allergies, restrictions, etc.)present in a cancer profile 112, provided by a physician, user, or thelike, and subtract each identified nutrition element 120 nutrient amountfrom nutrient amount 124 until a combination of nutritional elements 120that represents a solution is found. Once a solution is found, computingdevice 104 may generate a file of nutrition elements 120 and store in adatabase, as described in further detail below.

Continuing in reference to FIG. 1, generating combinations of nutritionelements 120 to achieve nutrient amounts 132 may include generating anobjective function. An “objective function,” as used in this disclosure,is a mathematical function that may be used by computing device 104 toscore each possible combination of nutrition elements 120, wherein theobjective function may refer to any mathematical optimization(mathematical programming) to select the ‘best’ element from a set ofavailable alternatives. Selecting the ‘best’ element from a set ofavailable alternatives may include a combination of nutrition elements120 which achieves the nutrient amounts 132 in addressing cancer profile112 in a user.

Continuing in reference to FIG. 1, an objective function may includeperforming a greedy algorithm process. A “greedy algorithm” is definedas an algorithm that selects locally optimal choices, which may or maynot generate a globally optimal solution. For instance, computing device104 may select combinations of nutrition elements 120 so that valuesassociated therewith are the best value for each category. For instance,in non-limiting illustrative example, optimization may determine thecombination of the most efficacious ‘serving size’, ‘timing ofconsumption’, ‘probiotic product’, ‘vegetable’, etc., categories toprovide a combination that may include several locally optimal solutionsbut may or may not be globally optimal in combination.

Still referring to FIG. 1, objective function may be formulated as alinear objective function, which computing device 104 may solve using alinear program, such as without limitation, a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint; alinear program may be referred to without limitation as a “linearoptimization” process and/or algorithm. For instance, in non-limitingillustrative examples, a given constraint might be a metabolic disorderof a user (e.g. lactose intolerance, poor absorption, food allergy, userpreference, etc.), and a linear program may use a linear objectivefunction to calculate combinations, considering how these limitationseffect combinations. In various embodiments, system 100 may determine aset of instructions towards addressing a subject's cancer profile 112that maximizes a total cancer prevention score subject to a constraintthat there are other competing objectives. For instance, if achievingone nutrient amount 124 by selecting from each nutrition element 120 mayresult in needing to select a second nutrition element 120, wherein eachmay compete in cancer prevention (e.g. adopting two or more diet typessimultaneously may not be feasible, a vegan option and a non-veganoption, etc.). A mathematical solver may be implemented to solve for theset of instructions that maximizes scores; mathematical solver may beimplemented on computing device 104 and/or another device in system 100,and/or may be implemented on third-party solver.

With continued reference to FIG. 1, objective function may includeminimizing a loss function, where a “loss function” is an expression anoutput of which a process minimizes to generate an optimal result. Forinstance, achieving nutrient amounts 132 may be set to a nominal value,such as ‘100’, wherein the objective function selects elements incombination that reduce the value to ‘0’, wherein the nutrient amounts132 are ‘100% achieved’. In such an example, ‘maximizing’ would beselecting the combination of nutrition elements 120 that results inachieving nutrient amounts 132 by minimizing the difference. As anon-limiting example, computing device 104 may assign variables relatingto a set of parameters, which may correspond to cancer preventioncomponents, calculate an output of mathematical expression using thevariables, and select an objective that produces an output having thelowest size, according to a given definition of “size.” Selection ofdifferent loss functions may result in identification of differentpotential combinations as generating minimal outputs, and thus‘maximizing’ efficacy of the combination.

Continuing in reference to FIG. 1, computing device 104 may usecalculated nutrient amounts 132 from nutrition machine learning model136 to determine nutrition elements 120 more precisely. For instance,computing device 104 may retrieve a variety of nutrition elements 120which contain particular vitamins, minerals, anti-inflammatorymolecules, phytonutrients, antioxidants, bioactive molecules, and thelike, which do not violate any other cancer prevention informationassociated with cancer profile 112. Computing device 104 maymix-and-match nutrition elements 120 to arrive at a particular calorieamount, or range of calories, while achieving nutrient amounts 132.

Continuing in reference to FIG. 1, computing device 104 is configured togenerate, using the plurality of nutrition elements 120, the canceralleviation nourishment plan. A “nourishment plan,” as used in thisdisclosure, is a collection of nutrient amounts 132 and nutritionelements 120 organized into a frequency (timing) and magnitude (servingsize) schedule. Nourishment plan 140 may include gathering, classifying,or otherwise categorizing nutrient amounts 132, nutrition elements 120lists, or the like, which incorporates cancer-specific recommendations.For instance, nutrition elements 120 may be scored with a numericalscore scale that associates a meal, beverage, supplement, etc., withpreventing cancer, benefit to cancer patient, and the like. Nourishmentplan 140 may include selecting nutrition elements 120 according to athreshold score, where items above are selected and arranged. Thresholdscore may include a daily threshold, wherein nutrition elements 120 areselected each day according to the threshold; and threshold may includea numerical value relating to cancer prevention, nutrient amount 132,among other outputs of system 100 described herein. Determiningnourishment plan 140 may include machine-learning. For instance,training a machine-learning model to identify a scoring rubric forbuilding the nourishment plan 140 based on some criteria such as cancerprevention, achieving remission, efficacy for helping maintainremission, among other criteria. Nourishment plan 140 may relatespecific cancers to specific nutrients of interest and provide nutritionelement 120 scheduling times and serving sizes for each meal.Nourishment plan 140 may differ from one user to the next according tothe magnitude of the disease outline (cancer category 124 and cancerprofile 112).

Continuing in reference to FIG. 1, nourishment plan 140 may include arecommended nutrition plan and a recommended supplement plan that atleast addresses cancer biomarker 108, mitigates symptoms, side-effects,etc. Nourishment plan 140 may contain a plan with timing of meals,calorie amounts, vitamin amounts, mineral amounts, etc. Nourishment plan140 may include food items combined with a supplement of non-food items.Nourishment plan 140 may be presented as a function of reversing,treating, and/or preventing cancer for non-cancer patients, for instancean otherwise healthy person to reduce their lifelong risk of cancer. Thelifelong risk of cancer may be enumerated in nourishment score 148. Sucha score may increase with participation in nourishment plan 140 and/ordecrease by falling short of nutrient amounts 132. Nourishment plan 140may include one or more treatment plans that incorporate, for instanceand without limitation, large quantities of acai berry and otherantioxidants, phytonutrients, and bioactive ingredients to preventoxidative damage that leads to the presence of free radicals.Nourishment plan 140 may be focused on achieving remission for a cancerpatient, where nourishment score 148 is tied to progression toremission, increasing with achieving remission, and again increasingwith each remission milestone.

Continuing in reference to FIG. 1, generating the cancer alleviationnourishment plan 140 may include generating a nourishment planclassifier using a nourishment classification machine-learning processto classify the plurality of nutrient amounts 132 to the plurality ofnutrition elements 120, and outputting the plurality of nutritionelements as a function of the nourishment plan classifier. Nourishmentplan classifier 144 may include any classifier as described abovegenerated by a classification machine-learning process using trainingdata, as described herein, performed by a machine-learning module asdescribed in further detail below. Training data for nourishment planclassifier 144 may include sets of data entries that include nutritionelements 120 (foods, supplements, recipes, etc.) that are correlated tonutrient amounts 132 of vitamins, minerals, phytonutrients,antioxidants, and the like, that classifier may be trained toautomatedly locate, sort, and output nutrition elements 120 according toa user's cancer category 124 and the nutrient amounts 132 they shouldreceive. Such training data may originate via a database, the Internet,research repository, and the like, as described above for training datafor other machine-learning processes. Nourishment plan classifier 144may accept an input of nutrient amounts 132 and output a plurality ofnutrition elements 120 with associated frequency (timing) and magnitude(serving size) schedule according to relationships between nutritionelements 120 and nutrient amounts 132. For instance and withoutlimitation, nourishment plan classifier 144 may contain relationshipsbetween individual fruits and vegetables, that when more vegetables areselected, certain fruits may not be necessary to schedule within thesame timeframe (day, meal, etc.). Such a classification process maydetermine a function, system of equations, and the like, which can besolved for in determining which nutrition elements 120 (fruits,vegetables, meats, dairy, grains, etc.) are useful to obtaining thenutrient amounts 132, while not missing some lower limits of nutrientamounts 132 (trace elements) and not exceeding upper limits for othernutrient amounts 132 (calories).

Continuing in reference to FIG. 1, generating the cancer alleviationnourishment plan 140 may include generating a nourishment score, whereinthe nourishment score reflects the level of user participation in thecancer alleviation nourishment plan 140. A “nourishment score,” as usedin this disclosure, reflects the level of user participation in thecancer alleviation nourishment plan. Nourishment score may include anumerical value, metric, parameter, and the like, described by afunction, vector, matrix, or any other mathematical arrangement.Nourishment score 148 may include enumerating a user's currentnourishment as it relates to cancer alleviation. Generating nourishmentscore 148 may include using a machine-learning process, algorithm,and/or model to derive a numerical scale along which to provide anumerical value according to a user's cancer profile 112 andparticipation in nourishment plan 140 generated from cancer profile 112.For instance, such a machine-learning model may be trained with trainingdata, wherein training data contains data entries of nutrient amounts132 correlated to cancer prevention. Such a machine-learning model withsaid training data may be used by computing device 104 to relate theconsumption of particular foods in nourishment plan 140, to achievingsome level of nutrient amount 132, and how the nutrient amount 132relates to cancer alleviation, achieving remission, maintainingremission, etc.

Continuing in reference to FIG. 1, in non-limiting illustratingexamples, falling short of vitamin E and vitamin K nutrient amounts 132,may have a particular effect on nourishment score 148 for an individualwho has been classified to “skin cancer” cancer category 124. Where,chronically falling short of the nutrient amount 132 results in a (−3score) each month but falling within the nutrient amount 132 range forthose two nutrients affords (+1 score for each) every month; the targetamount for the preceding month may dictate the score change for eachsubsequent month. In such a case, a machine-learning model may derive analgorithm which dictates the amount to increase/decrease nourishmentscore 148 for that particular cancer category 124 according to thenutrient amounts 132. In this case, the machine-learning model istrained to identify the relationship between nutrient amounts 132 andeffect on cancer prevention to derive an equation that relates scoringcriteria. The score is then calculated using the model and nutritiondata as an input. “Nutrition data,” as used in this disclosure, is datadescribing consumption by the user. Consumption by the user may includeamounts and identities of nutrition elements 120. In this way, system100 may calculate a nourishment score 148 as a function of a user'sparticipation in nourishment plan 140, where nourishment score 148 isupdated with each nutrition element 120 consumed by user.

Continuing in reference to FIG. 1, generating the cancer alleviationnourishment plan 140 may include calculating a change in incidence ofcancer as a function of adhering to nourishment plan 140. Calculating achange in incidence of cancer may include receiving nutritional inputfrom a user, for instance and without limitation, as described in Ser.No. 16/911,994, filed Jun. 25, 2020, titled “METHODS AND SYSTEMS FORADDITIVE MANUFACTURING OF NUTRITIONAL SUPPLEMENT SERVINGS,” the entiretyof which is incorporated herein by reference. System 100 may receivenutritional input from a user. “Nutritional input,” as used in thisdisclosure, is an amount of a nutrient consumed by a user. Nutritionalinput, for instance and without limitation, may include food items thathave associated nutrition facts, wherein computing device 104 maycalculate, weight, or otherwise modify, the nutritional input from theuser (e.g. with a weighting factor). This results in accurate, per-usernutritional input. That nutritional input can be used to determine (forinstance using subtraction) what amount of target nutrient amounts 132summarized in the nourishment plan 140 the user is consuming. Theadherence to the nourishment plan 140 is calculated from that, and theincidence of cancer may be determined from the adherence to thenourishment plan. Nutritional input of a user may include a designationof any nutrition elements 120 user may have consumed. Nutritionalelements 120 may have nutrient amounts 132 associated therewith, whichmay be applied to a user's current cancer profile 112, cancer category124, malignant parameters, and the like. Applying the nutrient amounts132 may include calculated a difference in nourishment score 148.Applying the nutrient amounts 132 may include calculating a change incancer risk, likelihood, or incidence as a function of achievingnutrient amounts 132, as described above, which may be enumerated innourishment score 148.

Continuing in reference to FIG. 1, generating the cancer alleviationnourishment plan 140 may include receiving a user preference regardingthe plurality of nutrition elements 120, and modifying the plurality ofnutrition elements 120 as a function of the user preference. A “userpreference,” as used in this disclosure, is a user input that designatesa preference related to at least a nutrition element 120. Userpreference 152 may include designations of nutrition elements 120 toavoid and/or include such as particular food groups, condiments, spices,dietary restrictions such as no animal products, cuisine type such asMediterranean foods, time of day for eating such as fasting before 10am, etc. In this way, computing device 104 may accept an input of userpreference 152 filter, sort, classify, or otherwise modify the datastructure of nutrition elements 120 and schedule the nutrition elements120 into nourishment plan 140 in a custom, per-user manner. Computingdevice 104 may modify the plurality of nutrition elements 120 as afunction of the user preference 144, for instance by providing recipeswith steps omitted, new steps added, or entirely new recipes altogetherutilizing the same or different nutrition elements 120. Computing device104 may modify the plurality of nutrition elements 120 as a function ofthe user preference 144 by generating a new file, based on thepreference, and storing and/or retrieving the file from a database, asdescribed in further detail below.

Referring now to FIG. 2, an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a subject andwritten in a programming language.

Still referring to FIG. 2, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailherein. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailherein; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedherein, such as a mathematical model, neural net, or program generatedby a machine learning algorithm known as a “classification algorithm,”as described in further detail herein, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 216 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of cancerbiomarkers 108 (such as gene expression patterns as it relates to cancerprofile 112) and/or other analyzed items and/or phenomena for which asubset of training data may be selected.

Still referring to FIG. 2, machine-learning module 200 may be configuredto perform a lazy-learning process 220 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofpredictions may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 204. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 204elements, such as classifying cancer biomarker 108 elements to cancerprofile 112 elements and assigning a value as a function of some rankingassociation between elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail herein.

Alternatively or additionally, and with continued reference to FIG. 2,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. A machine-learning model may be used to derivenumerical scales for providing numerical values to cancer profile 112and/or nourishment score 148, etc., as described above, to “learn” theupper and lower limits to the numerical scale, the increments toproviding scoring, and the criteria for increasing and decreasingelements encompassed in the cancer profile 112 and/or nourishment score148, etc. A machine-learning model may be used to “learn” which elementsof cancer biomarkers 108 have what effect on cancer profile 112, andwhich elements of cancer profile 112 are affected by particularnutrition elements 120 and the magnitude of effect, etc. The magnitudeof the effect may be enumerated and provided as part of system 100,where nutrition elements 120 are communicated to user for their cancerpreventative properties.

Still referring to FIG. 2, machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a cancer profile 112 (potentially classified into cancercategories 124), as described above as inputs, nutrient element 120outputs, and a ranking function representing a desired form ofrelationship to be detected between inputs and outputs; ranking functionmay, for instance, seek to maximize the probability that a given input(such as nutrient amounts 132) and/or combination of inputs isassociated with a given output (nourishment plan 140 that incorporatenutrient elements 120 to achieve nutrient amounts 132 that are ‘best’for cancer category 124) to minimize the probability that a given inputis not associated with a given output, for instance finding the mostsuitable times to consume meals, and what the meals should be, etc.Ranking function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 204. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 228 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 2, machine learning processes may include atleast an unsupervised machine-learning process 232. An unsupervisedmachine-learning process 232, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process 232 may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2, machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 2, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 204 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 204.

Referring now to FIG. 3, a non-limiting exemplary embodiment 300 of anourishment plan database 304 is illustrated. Cancer biomarker 108 for aplurality of subjects, for instance for generating a training dataclassifier 216, may be stored and/or retrieved in nourishment plandatabase 304. Cancer biomarker 108 data from a plurality of subjects forgenerating training data 204 may also be stored and/or retrieved from anourishment plan database 304. Computing device 104 may receive, store,and/or retrieve training data 204, wearable device data, physiologicalsensor data, biological extraction data, and the like, from nourishmentplan database 304. Computing device 104 may store and/or retrievenutrient machine-learning model 116, among other determinations, I/Odata, models, and the like, in nourishment plan database 304.

Continuing in reference to FIG. 3, nourishment plan database 304 may beimplemented, without limitation, as a relational database, a key-valueretrieval database such as a NOSQL database, or any other format orstructure for use as a database that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.Nourishment plan database 304 may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table and the like. Nourishmentplan database 304 may include a plurality of data entries and/orrecords, as described above. Data entries in a nourishment plan database304 may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina database may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistent with this disclosure.

Further referring to FIG. 3, nourishment plan database 304 may include,without limitation, cancer biomarker table 308, cancer profile table312, nutrition element table 316, nutrient amount table 320, nourishmentplan table 324, and/or heuristic table 328. Determinations by amachine-learning process, machine-learning model, ranking function,and/or classifier, may also be stored and/or retrieved from thenourishment plan database 304. As a non-limiting example, nourishmentplan database 304 may organize data according to one or more instructiontables. One or more nourishment plan database 304 tables may be linkedto one another by, for instance in a non-limiting example, common columnvalues. For instance, a common column between two tables of nourishmentplan database 304 may include an identifier of a submission, such as aform entry, textual submission, accessory device tokens, local accessaddresses, metrics, and the like, for instance as defined herein; as aresult, a search by a computing device 104 may be able to retrieve allrows from any table pertaining to a given submission or set thereof.Other columns may include any other category usable for organization orsubdivision of data, including types of data, names and/or identifiersof individuals submitting the data, times of submission, and the like;persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data from one or moretables may be linked and/or related to data in one or more other tables.

Continuing in reference to FIG. 3, in a non-limiting embodiment, one ormore tables of a nourishment plan database 304 may include, as anon-limiting example, a cancer biomarker table 308, which may includecategorized identifying data, as described above, including geneticdata, epigenetic data, microbiome data, physiological data, biologicalextraction data, and the like. Cancer biomarker table 308 may includecancer biomarker 108 categories according to gene expression patterns,SNPs, mutations, enzyme specific activity and concentration,phosphorylation data, proteasomal degradation data, data concerningmetabolism of nutrition elements 120, pharmacokinetics, nutrientabsorption, etc., categories, and may include linked tables tomathematical expressions that describe the impact of each cancerbiomarker 108 datum on cancer profile 112, for instance threshold valuesfor gene expression, etc., as it relates to cancer, cancer category 124,etc. One or more tables may include cancer profile table 312, which mayinclude data regarding cancer biomarker 108, thresholds, scores,metrics, values, categorizations, and the like, that system 100 may useto calculate, derive, filter, retrieve and/or store current cancerlevels, cancer types, likelihood of currently having a malignancy,probability of malignancy, metastasis, and the like. One or more tablesmay include nutrition element table 316, which may include data onnutrition elements 120 for instance classified to cancer category 124,classified to data from alike subjects with similar cancer biomarker108, cancer profile 112, and the like, that system 100 may use tocalculate, derive, filter, retrieve and/or store nutrition elements 120.One or more tables may include nutrient amount table 320, which mayinclude functions, model, equations, algorithms, and the like, using tocalculate or derive nutrient amounts 132 relating to cancer profile 112and/or cancer category 124, may include nutrient amounts 132 organizedby nutrient, nutrient classification, age, sex, cancer severity,remission, etc. One of more tables may include a nourishment plan table324, which may include nutrition element 120 identifiers, serving sizes,times associated with nutrition elements 120, regarding times to eat,identifiers of meals, recipes, ingredients, schedules, diet types, andthe like. One or more tables may include, without limitation, aheuristic table 328, which may organize rankings, scores, models,outcomes, functions, numerical values, scales, arrays, matrices, and thelike, that represent determinations, probabilities, metrics, parameters,values, and the like, include one or more inputs describing potentialmathematical relationships, as described herein.

Referring now to FIGS. 4A and 4B, a non-limiting exemplary embodiment400 of a cancer profile 112 is illustrated. Cancer profile 112 mayinclude a variety of cancer biomarker 108 categories, for instance 22distinct categories, as shown in FIGS. 4A and 4B. each cancer biomarker108 may be assigned a value, such as an arbitrary value, where somecancer biomarkers 108, such as those shaded in light grey, may relate toabsolute scales from [0, x], where x is a maximal value and the range ofvalues for the cancer biomarker 108 cannot be below a ‘zero amount’.Some cancer biomarkers 108, such as those shaded in dark grey, mayrelate to gene expression levels, wherein, the cancer biomarker 108 isenumerated as a ‘box plot’ that illustrates the range of expression in apopulation of users organized according to, for instance tissue type. Insuch an example, the dashed line may relate to a ‘normal threshold’above which is elevated gene expression, below which is decreasedexpression level. Each cancer biomarker 108 may have associated with ita numerical score, or some other identifying mathematical value thatcomputing device 104 may assign. Persons skilled in the art mayappreciate that for each user, any number of cancer biomarkers 108 maybe enumerated and assigned a value according to cancer profilemachine-learning model 116. Cancer profile 112 may be graphed, orotherwise displayed, according to the enumeration by cancer profilemachine-learning model 116. Each bar of the bar graph, or combinationsof bar graph categories, may instruct a classification of a user'scancer profile 112 to a cancer category 124.

Still referring now to FIGS. 4A and 4B, in non-limiting exemplaryillustrations cancer profile 112 may be classified to a cancer category124. Some and/or all of the cancer biomarkers 108 summarized in cancerprofile 112 may be used to classify an individual to a particular cancercategory 124. For instance, as shown in FIG. 4B, ten of the 22 cancerbiomarker 108 categories may be used to classify cancer profile 112 toone or more cancer categories 124. Alternatively or additionally, cancerprofile machine-learning model 116 may be trained to assign cancerbiomarker 108 to a cancer category 124, wherein computing device 104 mayknow the identity of cancer category 124 according to which cancercategory 124 has the most identifying data points.

Referring now to FIG. 5, a non-limiting exemplary embodiment 500 of acancer alleviation nourishment plan 140 is illustrated. Nourishment plan140 may include a schedule for arranging nutrition elements 120,according to for instance a 24-hour timetable, as designated on theleft, where consumption is planned along a user's typical day-nightcycle, beginning at ˜6 am until just after 6 pm. Nutrition element 120may include breakfast (denoted as mid-sized dark grey circle), which maycorrespond to a file of breakfast-related plurality of nutritionelements 120 (denoted b1, b2, b3, b4 . . . bn, to the nth breakfastitem). Nutrition element 120 may include snacks eaten throughout the dayto, for instance achieve nutrient amounts 132 missing from meals(denoted as small black circles), which may correspond to a file ofsnacking-related plurality of nutrition elements 120 (denoted s1, s2,s3, s4 . . . sn, to the nth snacking item). Nutrition element 120 mayinclude dinner (denoted as large-sized light grey circle), which maycorrespond to a file of dinner-related plurality of nutrition elements120 (denoted d1, d2, d3, d4 . . . dn, to the nth dinner item).Nourishment plan 140 may include a variety of diets, as denoted in themonthly schedule at the bottom, Sunday through Saturday. Nourishmentplan 140 ‘C’ is shown, which may be an idealistic goal for user toachieve by the end of the month, where nourishment plan ‘A’ and ‘B’ areintermediate plans intended to wean user to the ‘ideal’ plan. Nutritionelements 120 classified by ‘meal type’ may be further modified by ‘A’and ‘B’ according to user preferences 148 collected by computing device104 throughout the process. Circle sizes, denoting nutrition element 120classes may relate to portion sizes, which are graphed along the circlecorresponding to the times they are expected to be consumed. User mayindicate which nutrition element 120 from each category was consumed,and when it was consumed, to arrive at nourishment score 148.

Referring now to FIG. 6, a non-limiting exemplary embodiment 600 of auser device 604 is illustrated. User device 604 may include computingdevice 104, a “smartphone,” cellular mobile phone, desktop computer,laptop, tablet computer, internet-of-things (IOT) device, wearabledevice, among other devices. User device 604 may include any device thatis capable for communicating with computing device 104, nourishment plandatabase 304, or able to receive, transmit, and/or display, via agraphical user interface, cancer profile 112, nutrition element 120,nourishment plan 140, nourishment score 148, among other outputs fromsystem 100. User device 604 may provide a cancer profile 112, forinstance as a collection of metrics determined from cancer biomarker 108data. User device 604 may provide cancer category 124 that wasdetermined as a function of malignancy parameters enumerated in cancerprofile 112. User device 604 may provide data concerning nutrientamounts 132, including the levels of specific nutrients, nutrientranges, nutrients to avoid, etc. User device 604 may link timing offoods to preemptive ordering interface for ordering a nutrition element120, for instance and without limitation, through a designated mobileapplication, mapping tool or application, etc., and a radial searchmethod about a user's current location as described in U.S.Nonprovisional application Ser. No. 17/087,745, filed Nov. 3, 2020,titled “A METHOD FOR AND SYSTEM FOR PREDICTING ALIMENTARY ELEMENTORDERING BASED ON BIOLOGICAL EXTRACTION,” the entirety of which isincorporated herein by reference. User device 604 may display nutrientelements 120 as a function of location, for instance and withoutlimitation, as described in User device 604 may link nourishmentconsumption program 120 to a scheduling application, such as a‘calendar’ feature on user device, which may set audio-visualnotifications, timers, alarms, and the like. May select locations fornutrition elements 120 based on entity affinity to cancer research,cancer charity, etc.

Referring now to FIG. 7, an exemplary embodiment 700 of a method forgenerating a cancer alleviation nourishment plan is illustrated. At step705, the method including a computing device 104 configured forreceiving at least a cancer biomarker 108 relating to a user. Receivingat least the cancer biomarker 108 may include receiving a result of oneor more tests relating the user; this may be implemented, withoutlimitation, as described above in FIGS. 1-6.

Still referring to FIG. 7, at step 710, method includes retrieving, by acomputing device 104, a cancer profile 112 related to the user. of aplurality of malignancy parameters as a function of at least the cancerbiomarker 108. Retrieving the cancer profile 112 may include receivingcancer profile 112 training data, training a cancer profilemachine-learning model 116 with training data that includes a pluralityof data entries wherein each entry correlates cancer biomarkers 108 to aplurality of malignancy parameters, and generating the cancer profile112 as a function of the cancer profile machine-learning model 116 andat least the cancer biomarker; this may be implemented, withoutlimitation, as described above in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 715, method includesidentifying, by the computing device 104 and using the cancer profile112, a plurality of nutrition elements 120 for the user, whereinidentifying includes assigning the cancer profile 112 to a cancercategory 124, wherein the cancer category 124 is a determination about acurrent malignancy state of the user, calculating, according to thecancer category 124, a plurality of nutrient amounts 132, whereincalculating a plurality of nutrient amounts 132 includes determining aneffect of the plurality of nutrient amounts 132 on the cancer profile112, and calculating the plurality of nutrient amounts 132 as a functionof the effect, wherein the plurality of nutrient amounts 132 is aplurality of amounts intended to result in cancer prevention.Identifying, as a function of the plurality of nutrient amounts 132, theplurality of nutrition elements 120, wherein the plurality of nutritionelements 132 are intended to prevent cancer as a function of the cancercategory 124. Assigning the cancer profile 112 to a cancer category 124may include classifying the cancer profile 112 to a cancer category 124using a cancer classification machine-learning process 128 and assigningthe cancer category 124 as a function of the cancer classificationmachine-learning process 128 and the cancer profile 112. Determining theeffect of the plurality of nutrient amounts 132 on the cancer profile112 may include retrieving a plurality of predicted effects of theplurality of nutrient amounts 132 on the cancer profile 112 as afunction of at least the cancer biomarker 108. Calculating nutrientamounts 132 may include generating training data using the plurality ofpredicted effects of the plurality of nutrient amounts 132 identifiedaccording to the cancer category 124, training a nutritionmachine-learning model 136 according to the training data, whereintraining data includes a plurality of data entries that correlates themagnitude of nutrient effect to a plurality of nutrient amounts 132 foreach cancer category 124, and calculating nutrient amounts 132 as afunction of the nutrition machine learning model 136 and the cancercategory 124; this may be implemented, without limitation, as describedabove in FIGS. 1-6.

Continuing in reference to FIG. 1, at step 720, method includesgenerating, by the computing device 104, using the plurality ofnutrition elements 120, the cancer alleviation nourishment plan 140.Generating the cancer alleviation nourishment plan 140 may includegenerating a nourishment plan classifier 144 using a nourishmentclassification machine-learning process to classify the plurality ofnutrient amounts to the plurality of nutrition elements and outputtingthe plurality of nutrition elements 120 as a function of the nourishmentplan classifier 144. Generating the cancer alleviation nourishment plan140 may include generating a nourishment score 148, wherein thenourishment score 148 reflects the level of user participation in thecancer alleviation nourishment plan 140. Generating the canceralleviation nourishment plan 140 may include calculating a change inincidence of cancer as a function of adhering to nourishment plan 140.Generating the cancer alleviation nourishment plan 140 may includereceiving a user preference 152 related to the plurality of nutritionelements 120 and modifying the plurality of nutrition elements 120 as afunction of the user preference 152; this may be implemented, withoutlimitation, as described above in FIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system for generating a nourishment plan for a cancer state, thesystem comprising: a computing device, wherein the computing device isconfigured to: receive at least a cancer biomarker relating to a user,wherein the cancer biomarker consists of a biological molecule existingwithin a normal cell; retrieve a cancer profile related to the user;assign the cancer profile to a cancer category, wherein the cancercategory includes a determination of a current malignancy state of theuser; identify, using the cancer profile, a plurality of nutritionelements for the user, wherein identifying comprises: calculating,according to the cancer category, a plurality of nutrient amounts,wherein calculating the plurality of nutrient amounts includes:determining a respective effect of each nutrient amount of the pluralityof nutrient amounts on the cancer profile; and calculating each of thenutrient amounts of the plurality of nutrient amounts as a function ofthe respective effect of each the plurality of nutrient amounts, whereinthe plurality of nutrient amounts comprises a plurality of amountsintended to result in a change of incidence of cancer corresponding tothe cancer category; identifying, as a function of the plurality ofnutrient amounts, the plurality of nutrition elements for cancerprevention; and generate, using the plurality of nutrition elements, anourishment plan for a cancer state, the generating of the nourishmentplan comprising generating a nourishment score.
 2. The system of claim1, wherein receiving the at least the cancer biomarker further comprisesreceiving a result of one or more tests relating the user.
 3. The systemof claim 1, wherein retrieving the cancer profile further comprises:receiving cancer profile training data; training a cancer profilemachine-learning model with training data that includes a plurality ofdata entries wherein each entry correlates cancer biomarkers to aplurality of malignancy parameters; and generating the cancer profile asa function of the cancer profile machine-learning model and at least thecancer biomarker.
 4. The system of claim 1, wherein assigning the cancerprofile to a cancer category further comprises: classifying the cancerprofile to a cancer category using a cancer classificationmachine-learning process; and assigning the cancer category as afunction of the classifying.
 5. The system of claim 1, whereindetermining the effect of the plurality of nutrient amounts on thecancer profile further comprises retrieving a plurality of predictedeffects of the plurality of nutrient amounts on the cancer profile as afunction of at least the cancer biomarker.
 6. The system of claim 5,wherein calculating nutrient amounts further comprises: generatingtraining data using the plurality of predicted effects of the pluralityof nutrient amounts identified according to the cancer category;training a nutrition machine-learning model according to the trainingdata, wherein training data includes a plurality of data entries thatcorrelates the magnitude of nutrient effect to a plurality of nutrientamounts for each cancer category; and calculating nutrient amounts as afunction of the nutrition machine learning model and the cancercategory.
 7. The system of claim 1, wherein generating the nourishmentplan for a cancer state further comprises: generating a nourishment planclassifier using a nourishment classification machine-learning processto classify the plurality of nutrient amounts to the plurality ofnutrition elements; and outputting the plurality of nutrition elementsas a function of the nourishment plan classifier.
 8. (canceled)
 9. Thesystem of claim 1, wherein generating the nourishment plan for a cancerstate further comprises calculating a change in incidence of cancer as afunction of adherence to nourishment plan.
 10. The system of claim 1,wherein generating the nourishment plan for a cancer state nourishmentplan further comprises: receiving a user preference related to theplurality of nutrition elements; and modifying the plurality ofnutrition elements as a function of the user preference.
 11. A methodfor generating a nourishment plan for a cancer state, the methodcomprising: receiving, by a computing device, at least a cancerbiomarker relating to a user, wherein the cancer biomarker consists of abiological molecule existing within a normal cell; retrieving, by thecomputing device, a cancer profile related to the user; assigning, bythe computing device, the cancer profile to a cancer category, whereinthe cancer category includes a determination of a current malignancystate of the user; identifying, by the computing device, using thecancer profile, a plurality of nutrition elements for the user, whereinidentifying comprises: calculating, according to the cancer category, aplurality of nutrient amounts, wherein calculating the plurality ofnutrient amounts includes: determining a respective effect of eachnutrient amount of the plurality of nutrient amounts on the cancerprofile; and calculating each of the nutrient amounts of the pluralityof nutrient amounts as a function of the respective effect of each theplurality of nutrient amounts, wherein the plurality of nutrient amountscomprises a plurality of amounts intended to result in a change ofincidence of cancer corresponding to the cancer category; identifying,as a function of the plurality of nutrient amounts, the plurality ofnutrition elements for cancer prevention; and generating, by thecomputing device, using the plurality of nutrition elements, anourishment plan for a cancer state, the generating of the nourishmentplan comprising generating a nourishment score.
 12. The method of claim11, wherein receiving the at least the cancer biomarker furthercomprises receiving a result of one or more tests relating the user. 13.The method of claim 11, wherein retrieving the cancer profile furthercomprises: receiving cancer profile training data; training a cancerprofile machine-learning model with training data that includes aplurality of data entries wherein each entry correlates cancerbiomarkers to a plurality of malignancy parameters; and generating thecancer profile as a function of the cancer profile machine-learningmodel and at least the cancer biomarker.
 14. The method of claim 11,wherein assigning the cancer profile to a cancer category furthercomprises: classifying the cancer profile to a cancer category using acancer classification machine-learning process; and assigning the cancercategory as a function of the classifying.
 15. The method of claim 11,wherein determining the effect of the plurality of nutrient amounts onthe cancer profile further comprises retrieving a plurality of predictedeffects of the plurality of nutrient amounts on the cancer profile as afunction of at least the cancer biomarker.
 16. The method of claim 15,wherein calculating nutrient amounts further comprises: generatingtraining data using the plurality of predicted effects of the pluralityof nutrient amounts identified according to the cancer category;training a nutrition machine-learning model according to the trainingdata, wherein training data includes a plurality of data entries thatcorrelates the magnitude of nutrient effect to a plurality of nutrientamounts for each cancer category; and calculating nutrient amounts as afunction of the nutrition machine learning model and the cancercategory.
 17. The method of claim 11, wherein generating the nourishmentplan for a cancer state further comprises: generating a nourishment planclassifier using a nourishment classification machine-learning processto classify the plurality of nutrient amounts to the plurality ofnutrition elements; and outputting the plurality of nutrition elementsas a function of the nourishment plan classifier.
 18. (canceled)
 19. Themethod of claim 11, wherein generating the nourishment plan for a cancerstate further comprises calculating a change in incidence of cancer as afunction of adherence to nourishment plan.
 20. The method of claim 11,wherein generating the nourishment plan for a cancer state furthercomprises: receiving a user preference related to the plurality ofnutrition elements; and modifying the plurality of nutrition elements asa function of the user preference.